Automated medical diagnostics from X-ray imagery have the potential to reduce strain on clinicians in hospital settings [1]}, [2]}, and may even provide rapid insights into patient condition outside of the hospital [3]}. Deep learning (DL) and particularly convolutional neural networks (CNNs) have proven to be powerful tools for a wide range of computer vision tasks. Yet despite the advancement of CNNs, automated medical image assessment remains challenged by a lack of the massive expert-labeled datasets needed to successfully train such models. Extracting labels from existing clinical reports provides moderately sized labeled datasets for DL, but the process can be noisy and fails to provide the annotations needed for many high priority clinical tasks. Curating a medical dataset of comparable scale and quality to natural image datasets such as ImageNet (i.e., 1.4 million annotated images) is intractable because manual annotation of medical images by clinical experts is extremely time-consuming and expensive. In one study, for example, labeling pulmonary edema severity on 141 chest radiographs by 4 radiologists through a consensus process took approximately 25 man-hours [4]}. Therefore, efficient learning from limited datasets on the scale of 100 labeled examples will be necessary as research models continue to be developed and implemented in the complex and high-stakes medical domain.
